Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study
By Gustavo Cattelan Nobre, Elaine Tavares. Scientometrics, 2017
341 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Marketing, Strategy and Management
View Paper: https://www.semanticscholar.org/paper/dd4ffbaad5b6515d9098abfa077a1d2fd91e5e45
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
In Marketing
- Marco Perona
- Nicola Saccani
- Gianmarco Bressanelli
- Federico Adrodegari
- Sergio Terzi
- Patrick Mikalef
- Davide Chiaroni
- Claudio Sassanelli
In Strategy and Management
- Gianmarco Bressanelli
- Marco Perona
- Sergio Terzi
- Nicola Saccani
- Federico Adrodegari
- Patrick Mikalef
- Claudio Sassanelli
- Paolo Rosa
Citations in the corpus
- Exploring How Usage-Focused Business Models Enable Circular Economy through Digital Technologies (2018, methodology): “Consequently, the IoT technology may also support material tracking [6,29], playing a crucial role and contributing greatly to the collection of end-of-life products and waste management [5], thus helping to close the loop through reuse, remanufacturing, and recycling.”
- Assessing relations between Circular Economy and Industry 4.0: a systematic literature review (2019, background): “Even if BDA is generally known as one of the easiest ways to digitise the CE (Cattelan Nobre and Tavares 2017), other perspectives have been discussed, like exploiting it: (i) to develop automated (Antikainen, Uusitalo, and Kivikytö-Reponen 2018) x (Baines 2015) x (Bianchini et al. 2018) x…”
- The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies (2020, background): “As part of this COI and continuous improvement, it is necessary to have the ability to systematically search, analyze, and advance smart circular strategies to the highest possible levels of resource productivity and efficiency (EMF, 2016, 2019; Nobre & Tavares, 2017).”